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Eine Chance fürs Leben. Starte jetzt Dein Studium in Paderborn:

Foto: Universität Paderborn

Annika Junker

 Annika Junker

Fakultät für Maschinenbau

Gleichstellungsbeauftragte - Wissenschaftliche Mitarbeiterin - Gleichstellungsbeauftragte

Regelungstechnik und Mechatronik / Heinz Nixdorf Institut

Wissenschaftliche Mitarbeiterin

+49 5251 60-6291
+49 5251 60-6297
Fürstenallee 11
33102 Paderborn

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Data-Driven Models for Control Engineering Applications Using the Koopman Operator

A. Junker, J. Timmermann, A. Trächtler, in: 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022), 2022, pp. 1-9

Within this work, we investigate how data-driven numerical approximation methods of the Koopman operator can be used in practical control engineering applications. We refer to the method Extended Dynamic Mode Decomposition (EDMD), which approximates a nonlinear dynamical system as a linear model. This makes the method ideal for control engineering applications, because a linear system description is often assumed for this purpose. Using academic examples, we simulatively analyze the prediction performance of the learned EDMD models and show how relevant system properties like stability, controllability, and observability are reflected by the EDMD model, which is a critical requirement for a successful control design process. Subsequently, we present our experimental results on a mechatronic test bench and evaluate the applicability to the control engineering design process. As a result, the investigated methods are suitable as a low-effort alternative for the design steps of model building and adaptation in the classical model-based controller design method.

Learning Data-Driven PCHD Models for Control Engineering Applications

A. Junker, J. Timmermann, A. Trächtler, in: 14th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, 2022, pp. 389-394

The design of control engineering applications usually requires a model that accurately represents the dynamics of the real system. In addition to classical physical modeling, powerful data-driven approaches are increasingly used. However, the resulting models are not necessarily in a form that is advantageous for controller design. In the control engineering domain, it is highly beneficial if the system dynamics is given in PCHD form (Port-Controlled Hamiltonian Systems with Dissipation) because globally stable control laws can be easily realized while physical interpretability is guaranteed. In this work, we exploit the advantages of both strategies and present a new framework to obtain nonlinear high accurate system models in a data-driven way that are directly in PCHD form. We demonstrate the success of our method by model-based application on an academic example, as well as experimentally on a test bed.

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